Many search engine services, such as Google and Overture, provide for searching for information that is accessible via the Internet. These search engine services allow users to search for display pages, such as web pages, that may be of interest to users. After a user submits a search request (i.e., a query) that includes search terms, the search engine service identifies web pages that may be related to those search terms. To quickly identify related web pages, the search engine services may maintain a mapping of keywords to web pages. This mapping may be generated by “crawling” the web (i.e., the World Wide Web) to identify the keywords of each web page. To crawl the web, a search engine service may use a list of root web pages to identify all web pages that are accessible through those root web pages. The keywords of any particular web page can be identified using various well-known information retrieval techniques, such as identifying the words of a headline, the words supplied in the metadata of the web page, the words that are highlighted, and so on. The search engine service identifies web pages that may be related to the search request based on how well the keywords of a web page match the words of the query. The search engine service may then display to the user links to the identified web pages.
When the links are displayed, the search engine service may order the links to the web pages based on a ranking of the web pages that may be determined by their relevance to the query, popularity, importance, and/or some other measure. Relevance of a web page to a query may be determined using various techniques including a term frequency by inverse document frequency (“tf*idf”) metric, a cosine similarity metric, and so on. These techniques for determining relevance typically provide a text relevance score that is based on comparison of text of the web page to text of the query. Popularity of a web page may be derived from analysis of web page access information (e.g., number of different users who access a web page). Importance of a web page can be determined using various techniques including those described below. These techniques for determining importance may be considered as static in that they are based on the static structure of web pages and their links (i.e., a web graph) at some point in time. These techniques provide a static score to each web page that indicates its importance relative to other web pages.
Three well-known techniques for determining importance of a web page are PageRank, HITS (“Hyperlink-Induced Topic Search”), and DirectHIT. PageRank is based on the principle that web pages will have links to (i.e., “out links”) important web pages. Thus, the importance of a web page is based on the number and importance of other web pages that link to that web page (i.e., “in links”). In a simple form, the links between web pages can be represented by adjacency matrix A, where Aij represents the number of out links from web page i to web page j. The importance score wj for web page j can be represented by the following equation:wj=ΣiAijwi 
This equation can be solved by iterative calculations based on the following equation:ATw=w where w is the vector of importance scores for the web pages and is the principal eigenvector of AT.
Web pages are multimedia documents that include various media types such as text, images, video, and audio. The non-textual media types play an important role in conveying the information content of a web page to a user. Images in particular play an important role in conveying information to a user. The authors of web pages may prefer to express information as an image, rather than as text, because as the adage says, “a picture is worth a thousand words.” The authors may also prefer to use appealing images, rather than text, because the resulting web pages may be more attractive and may be perceived to be of higher quality. As described above, typical techniques for ranking web pages factor in textual relevance and static importance. These techniques, however, typically do not factor in the information associated with or based on the non-textual media types.